{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic mixed precision training and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to apply the automatic mixed precision (AMP) feature of PyTorch to training and validation programs.  \n",
    "It's modified from the Spleen 3D segmentation tutorial notebook, and compares the training speed and memory usage with/without AMP.\n",
    "\n",
    "The Spleen dataset can be downloaded from http://medicaldecathlon.com/."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.4.dev2426\n",
      "Numpy version: 1.26.4\n",
      "Pytorch version: 2.3.1+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: d622a16f927841fdd7d057b7553805405f0805e4\n",
      "MONAI __file__: /home/<username>/anaconda3/envs/monai/lib/python3.9/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.24.0\n",
      "scipy version: 1.13.1\n",
      "Pillow version: 10.4.0\n",
      "Tensorboard version: 2.17.0\n",
      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "TorchVision version: 0.18.1+cu121\n",
      "tqdm version: 4.66.4\n",
      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "psutil version: 6.0.0\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: 2.14.2\n",
      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, DataLoader, decollate_batch\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.transforms import (\n",
    "    EnsureChannelFirstd,\n",
    "    AsDiscrete,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    FgBgToIndicesd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    ")\n",
    "from monai.utils import get_torch_version_tuple, set_determinism\n",
    "\n",
    "print_config()\n",
    "\n",
    "if get_torch_version_tuple() < (1, 6):\n",
    "    raise RuntimeError(\"AMP feature only exists in PyTorch version greater than v1.6.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root dir is: /home/chyang/Documents/monai_tutorials_gitee/data\n"
     ]
    }
   ],
   "source": [
    "os.environ['MONAI_DATA_DIRECTORY'] = '/home/chyang/Documents/monai_tutorials_gitee/data'\n",
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "if directory is not None:\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(f\"root dir is: {root_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the Decathlon Spleen dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_root = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "if not os.path.exists(data_root):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = sorted(glob.glob(os.path.join(data_root, \"imagesTr\", \"*.nii.gz\")))\n",
    "train_labels = sorted(glob.glob(os.path.join(data_root, \"labelsTr\", \"*.nii.gz\")))\n",
    "data_dicts = [{\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(train_images, train_labels)]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformations():\n",
    "    train_transforms = Compose(\n",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            # pre-compute foreground and background indexes\n",
    "            # and cache them to accelerate training\n",
    "            FgBgToIndicesd(\n",
    "                keys=\"label\",\n",
    "                fg_postfix=\"_fg\",\n",
    "                bg_postfix=\"_bg\",\n",
    "                image_key=\"image\",\n",
    "            ),\n",
    "            # randomly crop out patch samples from\n",
    "            # big image based on pos / neg ratio\n",
    "            # the image centers of negative samples must be in valid image area\n",
    "            RandCropByPosNegLabeld(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                label_key=\"label\",\n",
    "                spatial_size=(128, 128, 96),\n",
    "                pos=1,\n",
    "                neg=1,\n",
    "                num_samples=4,\n",
    "                fg_indices_key=\"label_fg\",\n",
    "                bg_indices_key=\"label_bg\",\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "    val_transforms = Compose(\n",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        ]\n",
    "    )\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process\n",
    "Usage of PyTorch AMP module refers to: https://pytorch.org/docs/stable/notes/amp_examples.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(amp=False):\n",
    "    train_trans, val_trans = transformations()\n",
    "    train_ds = CacheDataset(data=train_files, transform=train_trans, cache_rate=1.0, num_workers=8)\n",
    "    val_ds = CacheDataset(data=val_files, transform=val_trans, cache_rate=1.0, num_workers=5)\n",
    "\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = DataLoader(\n",
    "        train_ds,\n",
    "        batch_size=1,\n",
    "        shuffle=True,\n",
    "        num_workers=1,\n",
    "    )\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=1)\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(\n",
    "        spatial_dims=3,\n",
    "        in_channels=1,\n",
    "        out_channels=2,\n",
    "        channels=(16, 32, 64, 128, 256),\n",
    "        strides=(2, 2, 2, 2),\n",
    "        num_res_units=2,\n",
    "        norm=Norm.BATCH,\n",
    "    ).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "    scaler = torch.cuda.amp.GradScaler() if amp else None\n",
    "\n",
    "    post_pred = Compose([AsDiscrete(argmax=True, to_onehot=2)])\n",
    "    post_label = Compose([AsDiscrete(to_onehot=2)])\n",
    "\n",
    "    dice_metric = DiceMetric(include_background=False, reduction=\"mean\", get_not_nans=False)\n",
    "\n",
    "    max_epochs = 20\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    best_metrics_epochs_and_time = [[], [], []]\n",
    "    epoch_loss_values = []\n",
    "    metric_values = []\n",
    "    epoch_times = []\n",
    "    total_start = time.time()\n",
    "    for epoch in range(max_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = (\n",
    "                batch_data[\"image\"].to(device),\n",
    "                batch_data[\"label\"].to(device),\n",
    "            )\n",
    "            optimizer.zero_grad()\n",
    "            if amp and scaler is not None:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                    outputs = model(inputs)\n",
    "                    loss = loss_function(outputs, labels)\n",
    "                scaler.scale(loss).backward()\n",
    "                scaler.step(optimizer)\n",
    "                scaler.update()\n",
    "            else:\n",
    "                outputs = model(inputs)\n",
    "                loss = loss_function(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print(\n",
    "                f\"{step}/{len(train_ds) // train_loader.batch_size},\"\n",
    "                f\" train_loss: {loss.item():.4f}\"\n",
    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
    "            )\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = (\n",
    "                        val_data[\"image\"].to(device),\n",
    "                        val_data[\"label\"].to(device),\n",
    "                    )\n",
    "                    roi_size = (160, 160, 128)\n",
    "                    sw_batch_size = 4\n",
    "                    if amp:\n",
    "                        with torch.cuda.amp.autocast():\n",
    "                            val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                    else:\n",
    "                        val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                    val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                    val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                    dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "                metric = dice_metric.aggregate().item()\n",
    "                dice_metric.reset()\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    best_metrics_epochs_and_time[0].append(best_metric)\n",
    "                    best_metrics_epochs_and_time[1].append(best_metric_epoch)\n",
    "                    best_metrics_epochs_and_time[2].append(time.time() - total_start)\n",
    "                    torch.save(model.state_dict(), \"best_metric_model.pth\")\n",
    "                    print(\"saved new best metric model\")\n",
    "                print(\n",
    "                    f\"current epoch: {epoch + 1} current\"\n",
    "                    f\" mean dice: {metric:.4f}\"\n",
    "                    f\" best mean dice: {best_metric:.4f} \"\n",
    "                    f\"at epoch: {best_metric_epoch}\"\n",
    "                )\n",
    "        print(f\"time consuming of epoch {epoch + 1}\" f\" is: {(time.time() - epoch_start):.4f}\")\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "    print(\n",
    "        f\"train completed, best_metric: {best_metric:.4f}\"\n",
    "        f\" at epoch: {best_metric_epoch}\"\n",
    "        f\" total time: {(time.time() - total_start):.4f}\"\n",
    "    )\n",
    "    return (\n",
    "        max_epochs,\n",
    "        epoch_loss_values,\n",
    "        metric_values,\n",
    "        epoch_times,\n",
    "        best_metrics_epochs_and_time,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 32/32 [00:19<00:00,  1.61it/s]\n",
      "Loading dataset: 100%|██████████| 9/9 [00:04<00:00,  2.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/32, train_loss: 0.7011 step time: 0.1140\n",
      "2/32, train_loss: 0.6614 step time: 0.0715\n",
      "3/32, train_loss: 0.6836 step time: 0.0616\n",
      "4/32, train_loss: 0.6778 step time: 0.0592\n",
      "5/32, train_loss: 0.6846 step time: 0.0573\n",
      "6/32, train_loss: 0.6916 step time: 0.0585\n",
      "7/32, train_loss: 0.6937 step time: 0.0585\n",
      "8/32, train_loss: 0.6883 step time: 0.0588\n",
      "9/32, train_loss: 0.6890 step time: 0.0582\n",
      "10/32, train_loss: 0.6849 step time: 0.0582\n",
      "11/32, train_loss: 0.6859 step time: 0.0590\n",
      "12/32, train_loss: 0.6932 step time: 0.0590\n",
      "13/32, train_loss: 0.6513 step time: 0.0601\n",
      "14/32, train_loss: 0.6795 step time: 0.0577\n",
      "15/32, train_loss: 0.6714 step time: 0.0597\n",
      "16/32, train_loss: 0.6745 step time: 0.0600\n",
      "17/32, train_loss: 0.6713 step time: 0.0595\n",
      "18/32, train_loss: 0.6778 step time: 0.0577\n",
      "19/32, train_loss: 0.6466 step time: 0.0576\n",
      "20/32, train_loss: 0.6473 step time: 0.0570\n",
      "21/32, train_loss: 0.6832 step time: 0.0593\n",
      "22/32, train_loss: 0.6522 step time: 0.0594\n",
      "23/32, train_loss: 0.6638 step time: 0.0585\n",
      "24/32, train_loss: 0.6762 step time: 0.0583\n",
      "25/32, train_loss: 0.6548 step time: 0.0579\n",
      "26/32, train_loss: 0.6408 step time: 0.0580\n",
      "27/32, train_loss: 0.6486 step time: 0.0581\n",
      "28/32, train_loss: 0.6509 step time: 0.0578\n",
      "29/32, train_loss: 0.6690 step time: 0.0580\n",
      "30/32, train_loss: 0.6482 step time: 0.0582\n",
      "31/32, train_loss: 0.6503 step time: 0.0587\n",
      "32/32, train_loss: 0.6160 step time: 0.0565\n",
      "epoch 1 average loss: 0.6690\n",
      "saved new best metric model\n",
      "current epoch: 1 current mean dice: 0.0328 best mean dice: 0.0328 at epoch: 1\n",
      "time consuming of epoch 1 is: 4.1784\n",
      "----------\n",
      "epoch 2/20\n",
      "1/32, train_loss: 0.5927 step time: 0.0610\n",
      "2/32, train_loss: 0.6473 step time: 0.0576\n",
      "3/32, train_loss: 0.6539 step time: 0.0613\n",
      "4/32, train_loss: 0.6245 step time: 0.0579\n",
      "5/32, train_loss: 0.6625 step time: 0.0569\n",
      "6/32, train_loss: 0.6515 step time: 0.0563\n",
      "7/32, train_loss: 0.6515 step time: 0.0574\n",
      "8/32, train_loss: 0.6376 step time: 0.0579\n",
      "9/32, train_loss: 0.6397 step time: 0.0565\n",
      "10/32, train_loss: 0.6560 step time: 0.0588\n",
      "11/32, train_loss: 0.6324 step time: 0.0610\n",
      "12/32, train_loss: 0.6468 step time: 0.0599\n",
      "13/32, train_loss: 0.5759 step time: 0.0591\n",
      "14/32, train_loss: 0.6687 step time: 0.0606\n",
      "15/32, train_loss: 0.5766 step time: 0.0630\n",
      "16/32, train_loss: 0.6262 step time: 0.0659\n",
      "17/32, train_loss: 0.6532 step time: 0.0672\n",
      "18/32, train_loss: 0.6477 step time: 0.0595\n",
      "19/32, train_loss: 0.6489 step time: 0.0574\n",
      "20/32, train_loss: 0.5660 step time: 0.0578\n",
      "21/32, train_loss: 0.6522 step time: 0.0565\n",
      "22/32, train_loss: 0.6280 step time: 0.0578\n",
      "23/32, train_loss: 0.6350 step time: 0.0701\n",
      "24/32, train_loss: 0.6273 step time: 0.0581\n",
      "25/32, train_loss: 0.6300 step time: 0.0595\n",
      "26/32, train_loss: 0.6465 step time: 0.0629\n",
      "27/32, train_loss: 0.6519 step time: 0.0604\n",
      "28/32, train_loss: 0.6086 step time: 0.0610\n",
      "29/32, train_loss: 0.6207 step time: 0.0604\n",
      "30/32, train_loss: 0.6544 step time: 0.0591\n",
      "31/32, train_loss: 0.6290 step time: 0.0574\n",
      "32/32, train_loss: 0.6293 step time: 0.0622\n",
      "epoch 2 average loss: 0.6335\n",
      "saved new best metric model\n",
      "current epoch: 2 current mean dice: 0.0458 best mean dice: 0.0458 at epoch: 2\n",
      "time consuming of epoch 2 is: 4.2358\n",
      "----------\n",
      "epoch 3/20\n",
      "1/32, train_loss: 0.5778 step time: 0.0614\n",
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      "32/32, train_loss: 0.6367 step time: 0.0576\n",
      "epoch 3 average loss: 0.6177\n",
      "saved new best metric model\n",
      "current epoch: 3 current mean dice: 0.0568 best mean dice: 0.0568 at epoch: 3\n",
      "time consuming of epoch 3 is: 4.2665\n",
      "----------\n",
      "epoch 4/20\n",
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      "32/32, train_loss: 0.6124 step time: 0.0573\n",
      "epoch 4 average loss: 0.5987\n",
      "saved new best metric model\n",
      "current epoch: 4 current mean dice: 0.0708 best mean dice: 0.0708 at epoch: 4\n",
      "time consuming of epoch 4 is: 4.2898\n",
      "----------\n",
      "epoch 5/20\n",
      "1/32, train_loss: 0.5993 step time: 0.0603\n",
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      "epoch 5 average loss: 0.5843\n",
      "current epoch: 5 current mean dice: 0.0663 best mean dice: 0.0708 at epoch: 4\n",
      "time consuming of epoch 5 is: 4.3098\n",
      "----------\n",
      "epoch 6/20\n",
      "1/32, train_loss: 0.5251 step time: 0.0608\n",
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      "32/32, train_loss: 0.5589 step time: 0.0605\n",
      "epoch 6 average loss: 0.5708\n",
      "saved new best metric model\n",
      "current epoch: 6 current mean dice: 0.0763 best mean dice: 0.0763 at epoch: 6\n",
      "time consuming of epoch 6 is: 4.3267\n",
      "----------\n",
      "epoch 7/20\n",
      "1/32, train_loss: 0.5888 step time: 0.0612\n",
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      "32/32, train_loss: 0.5283 step time: 0.0611\n",
      "epoch 7 average loss: 0.5580\n",
      "saved new best metric model\n",
      "current epoch: 7 current mean dice: 0.0893 best mean dice: 0.0893 at epoch: 7\n",
      "time consuming of epoch 7 is: 4.4713\n",
      "----------\n",
      "epoch 8/20\n",
      "1/32, train_loss: 0.5616 step time: 0.0633\n",
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      "epoch 8 average loss: 0.5436\n",
      "current epoch: 8 current mean dice: 0.0818 best mean dice: 0.0893 at epoch: 7\n",
      "time consuming of epoch 8 is: 4.4459\n",
      "----------\n",
      "epoch 9/20\n",
      "1/32, train_loss: 0.5895 step time: 0.0598\n",
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      "epoch 9 average loss: 0.5320\n",
      "current epoch: 9 current mean dice: 0.0756 best mean dice: 0.0893 at epoch: 7\n",
      "time consuming of epoch 9 is: 4.3974\n",
      "----------\n",
      "epoch 10/20\n",
      "1/32, train_loss: 0.4865 step time: 0.0581\n",
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      "epoch 10 average loss: 0.5286\n",
      "current epoch: 10 current mean dice: 0.0862 best mean dice: 0.0893 at epoch: 7\n",
      "time consuming of epoch 10 is: 4.3589\n",
      "----------\n",
      "epoch 11/20\n",
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      "epoch 11 average loss: 0.5200\n",
      "saved new best metric model\n",
      "current epoch: 11 current mean dice: 0.0919 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 11 is: 4.3153\n",
      "----------\n",
      "epoch 12/20\n",
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      "epoch 12 average loss: 0.5074\n",
      "current epoch: 12 current mean dice: 0.0630 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 12 is: 4.1122\n",
      "----------\n",
      "epoch 13/20\n",
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      "epoch 13 average loss: 0.4928\n",
      "current epoch: 13 current mean dice: 0.0670 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 13 is: 4.0615\n",
      "----------\n",
      "epoch 14/20\n",
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      "epoch 14 average loss: 0.4868\n",
      "current epoch: 14 current mean dice: 0.0674 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 14 is: 4.5238\n",
      "----------\n",
      "epoch 15/20\n",
      "1/32, train_loss: 0.4844 step time: 0.0612\n",
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      "epoch 15 average loss: 0.4768\n",
      "current epoch: 15 current mean dice: 0.0679 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 15 is: 4.2613\n",
      "----------\n",
      "epoch 16/20\n",
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      "epoch 16 average loss: 0.4675\n",
      "current epoch: 16 current mean dice: 0.0684 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 16 is: 3.9905\n",
      "----------\n",
      "epoch 17/20\n",
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      "epoch 17 average loss: 0.4563\n",
      "current epoch: 17 current mean dice: 0.0810 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 17 is: 4.0565\n",
      "----------\n",
      "epoch 18/20\n",
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      "epoch 18 average loss: 0.4269\n",
      "current epoch: 18 current mean dice: 0.0504 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 18 is: 4.4125\n",
      "----------\n",
      "epoch 19/20\n",
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      "epoch 19 average loss: 0.4213\n",
      "current epoch: 19 current mean dice: 0.0514 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 19 is: 4.2686\n",
      "----------\n",
      "epoch 20/20\n",
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      "19/32, train_loss: 0.3761 step time: 0.0601\n",
      "20/32, train_loss: 0.3023 step time: 0.0603\n",
      "21/32, train_loss: 0.4913 step time: 0.0609\n",
      "22/32, train_loss: 0.4023 step time: 0.0602\n",
      "23/32, train_loss: 0.4204 step time: 0.0611\n",
      "24/32, train_loss: 0.4144 step time: 0.0573\n",
      "25/32, train_loss: 0.3343 step time: 0.0611\n",
      "26/32, train_loss: 0.4523 step time: 0.0716\n",
      "27/32, train_loss: 0.4675 step time: 0.0578\n",
      "28/32, train_loss: 0.3971 step time: 0.0600\n",
      "29/32, train_loss: 0.3036 step time: 0.0706\n",
      "30/32, train_loss: 0.4099 step time: 0.0704\n",
      "31/32, train_loss: 0.4153 step time: 0.0649\n",
      "32/32, train_loss: 0.3275 step time: 0.0584\n",
      "epoch 20 average loss: 0.4085\n",
      "current epoch: 20 current mean dice: 0.0355 best mean dice: 0.0919 at epoch: 11\n",
      "time consuming of epoch 20 is: 4.1484\n",
      "train completed, best_metric: 0.0919 at epoch: 11 total time: 85.4311\n",
      "total training time of 20 epochs with AMP: 109.9521\n"
     ]
    }
   ],
   "source": [
    "set_determinism(seed=0)\n",
    "amp_start = time.time()\n",
    "(\n",
    "    max_epochs,\n",
    "    amp_epoch_loss_values,\n",
    "    amp_metric_values,\n",
    "    amp_epoch_times,\n",
    "    amp_best,\n",
    ") = train_process(amp=True)\n",
    "amp_total_time = time.time() - amp_start\n",
    "print(f\"total training time of {max_epochs} \" f\"epochs with AMP: {amp_total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NVIDIA GeForce RTX 3080 Ti\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      | 320420 KiB |   1812 MiB |   6949 GiB |   6949 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         | 320420 KiB |   1812 MiB |   6949 GiB |   6949 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Requested memory      | 318667 KiB |   1807 MiB |   6933 GiB |   6932 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |   2706 MiB |   2706 MiB |   2706 MiB |      0 B   |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory | 461916 KiB |    884 MiB |   5502 GiB |   5502 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     449    |     995    |     825 K  |     824 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     449    |     995    |     825 K  |     824 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      50    |      50    |      50    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      32    |      75    |  397049    |  397017    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize allocations  |       0    |       0    |       0    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize GPU segments |       0    |       0    |       0    |       0    |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train without AMP\n",
    "In order to correctly measure the memory usage, please restart the notebook and skip above AMP training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 32/32 [00:20<00:00,  1.59it/s]\n",
      "Loading dataset: 100%|██████████| 9/9 [00:04<00:00,  1.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/32, train_loss: 0.7011 step time: 0.2187\n",
      "2/32, train_loss: 0.6614 step time: 0.1446\n",
      "3/32, train_loss: 0.6836 step time: 0.1366\n",
      "4/32, train_loss: 0.6778 step time: 0.1377\n",
      "5/32, train_loss: 0.6846 step time: 0.1418\n",
      "6/32, train_loss: 0.6916 step time: 0.1367\n",
      "7/32, train_loss: 0.6937 step time: 0.1388\n",
      "8/32, train_loss: 0.6883 step time: 0.1387\n",
      "9/32, train_loss: 0.6890 step time: 0.1409\n",
      "10/32, train_loss: 0.6849 step time: 0.1442\n",
      "11/32, train_loss: 0.6859 step time: 0.1392\n",
      "12/32, train_loss: 0.6932 step time: 0.1401\n",
      "13/32, train_loss: 0.6512 step time: 0.1393\n",
      "14/32, train_loss: 0.6795 step time: 0.2551\n",
      "15/32, train_loss: 0.6714 step time: 0.2405\n",
      "16/32, train_loss: 0.6745 step time: 0.1417\n",
      "17/32, train_loss: 0.6713 step time: 0.1429\n",
      "18/32, train_loss: 0.6778 step time: 0.1413\n",
      "19/32, train_loss: 0.6466 step time: 0.1416\n",
      "20/32, train_loss: 0.6473 step time: 0.1418\n",
      "21/32, train_loss: 0.6832 step time: 0.1402\n",
      "22/32, train_loss: 0.6522 step time: 0.1431\n",
      "23/32, train_loss: 0.6638 step time: 0.1444\n",
      "24/32, train_loss: 0.6762 step time: 0.1444\n",
      "25/32, train_loss: 0.6548 step time: 0.1443\n",
      "26/32, train_loss: 0.6408 step time: 0.1457\n",
      "27/32, train_loss: 0.6486 step time: 0.1481\n",
      "28/32, train_loss: 0.6509 step time: 0.1462\n",
      "29/32, train_loss: 0.6690 step time: 0.1427\n",
      "30/32, train_loss: 0.6482 step time: 0.1449\n",
      "31/32, train_loss: 0.6503 step time: 0.1422\n",
      "32/32, train_loss: 0.6160 step time: 0.1424\n",
      "epoch 1 average loss: 0.6690\n",
      "saved new best metric model\n",
      "current epoch: 1 current mean dice: 0.0328 best mean dice: 0.0328 at epoch: 1\n",
      "time consuming of epoch 1 is: 8.8064\n",
      "----------\n",
      "epoch 2/20\n",
      "1/32, train_loss: 0.5927 step time: 0.1418\n",
      "2/32, train_loss: 0.6473 step time: 0.1422\n",
      "3/32, train_loss: 0.6539 step time: 0.1628\n",
      "4/32, train_loss: 0.6245 step time: 0.1568\n",
      "5/32, train_loss: 0.6625 step time: 0.1448\n",
      "6/32, train_loss: 0.6515 step time: 0.1461\n",
      "7/32, train_loss: 0.6515 step time: 0.1686\n",
      "8/32, train_loss: 0.6376 step time: 0.1477\n",
      "9/32, train_loss: 0.6397 step time: 0.1542\n",
      "10/32, train_loss: 0.6560 step time: 0.1589\n",
      "11/32, train_loss: 0.6324 step time: 0.1461\n",
      "12/32, train_loss: 0.6468 step time: 0.1478\n",
      "13/32, train_loss: 0.5759 step time: 0.1466\n",
      "14/32, train_loss: 0.6688 step time: 0.1446\n",
      "15/32, train_loss: 0.5766 step time: 0.1442\n",
      "16/32, train_loss: 0.6262 step time: 0.1502\n",
      "17/32, train_loss: 0.6532 step time: 0.1495\n",
      "18/32, train_loss: 0.6477 step time: 0.1512\n",
      "19/32, train_loss: 0.6490 step time: 0.1487\n",
      "20/32, train_loss: 0.5660 step time: 0.1477\n",
      "21/32, train_loss: 0.6522 step time: 0.1481\n",
      "22/32, train_loss: 0.6280 step time: 0.1464\n",
      "23/32, train_loss: 0.6350 step time: 0.1476\n",
      "24/32, train_loss: 0.6272 step time: 0.1458\n",
      "25/32, train_loss: 0.6300 step time: 0.1458\n",
      "26/32, train_loss: 0.6465 step time: 0.1488\n",
      "27/32, train_loss: 0.6519 step time: 0.1478\n",
      "28/32, train_loss: 0.6086 step time: 0.1510\n",
      "29/32, train_loss: 0.6207 step time: 0.1491\n",
      "30/32, train_loss: 0.6544 step time: 0.1489\n",
      "31/32, train_loss: 0.6290 step time: 0.1481\n",
      "32/32, train_loss: 0.6293 step time: 0.1457\n",
      "epoch 2 average loss: 0.6335\n",
      "saved new best metric model\n",
      "current epoch: 2 current mean dice: 0.0458 best mean dice: 0.0458 at epoch: 2\n",
      "time consuming of epoch 2 is: 8.8477\n",
      "----------\n",
      "epoch 3/20\n",
      "1/32, train_loss: 0.5778 step time: 0.1400\n",
      "2/32, train_loss: 0.6004 step time: 0.1606\n",
      "3/32, train_loss: 0.6442 step time: 0.1639\n",
      "4/32, train_loss: 0.6316 step time: 0.1504\n",
      "5/32, train_loss: 0.6422 step time: 0.1529\n",
      "6/32, train_loss: 0.6382 step time: 0.1702\n",
      "7/32, train_loss: 0.6290 step time: 0.1574\n",
      "8/32, train_loss: 0.6184 step time: 0.1507\n",
      "9/32, train_loss: 0.6467 step time: 0.1496\n",
      "10/32, train_loss: 0.5792 step time: 0.1481\n",
      "11/32, train_loss: 0.5963 step time: 0.1508\n",
      "12/32, train_loss: 0.6401 step time: 0.1472\n",
      "13/32, train_loss: 0.6319 step time: 0.1476\n",
      "14/32, train_loss: 0.5889 step time: 0.1489\n",
      "15/32, train_loss: 0.6140 step time: 0.1482\n",
      "16/32, train_loss: 0.6458 step time: 0.1481\n",
      "17/32, train_loss: 0.6306 step time: 0.1484\n",
      "18/32, train_loss: 0.5927 step time: 0.1469\n",
      "19/32, train_loss: 0.6461 step time: 0.1497\n",
      "20/32, train_loss: 0.6380 step time: 0.1503\n",
      "21/32, train_loss: 0.5927 step time: 0.1479\n",
      "22/32, train_loss: 0.5981 step time: 0.1485\n",
      "23/32, train_loss: 0.6393 step time: 0.1485\n",
      "24/32, train_loss: 0.6112 step time: 0.1484\n",
      "25/32, train_loss: 0.6367 step time: 0.1473\n",
      "26/32, train_loss: 0.5736 step time: 0.1479\n",
      "27/32, train_loss: 0.6207 step time: 0.1480\n",
      "28/32, train_loss: 0.6116 step time: 0.1471\n",
      "29/32, train_loss: 0.6165 step time: 0.1462\n",
      "30/32, train_loss: 0.5941 step time: 0.1471\n",
      "31/32, train_loss: 0.6029 step time: 0.1478\n",
      "32/32, train_loss: 0.6366 step time: 0.1466\n",
      "epoch 3 average loss: 0.6177\n",
      "saved new best metric model\n",
      "current epoch: 3 current mean dice: 0.0570 best mean dice: 0.0570 at epoch: 3\n",
      "time consuming of epoch 3 is: 8.6383\n",
      "----------\n",
      "epoch 4/20\n",
      "1/32, train_loss: 0.5779 step time: 0.1409\n",
      "2/32, train_loss: 0.6230 step time: 0.1637\n",
      "3/32, train_loss: 0.6234 step time: 0.1813\n",
      "4/32, train_loss: 0.6301 step time: 0.1493\n",
      "5/32, train_loss: 0.5891 step time: 0.1689\n",
      "6/32, train_loss: 0.6187 step time: 0.1590\n",
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      "10/32, train_loss: 0.5823 step time: 0.1524\n",
      "11/32, train_loss: 0.6268 step time: 0.1480\n",
      "12/32, train_loss: 0.6014 step time: 0.1895\n",
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      "17/32, train_loss: 0.6240 step time: 0.1491\n",
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      "26/32, train_loss: 0.5799 step time: 0.1703\n",
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      "28/32, train_loss: 0.6222 step time: 0.1504\n",
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      "30/32, train_loss: 0.5730 step time: 0.1502\n",
      "31/32, train_loss: 0.6050 step time: 0.1464\n",
      "32/32, train_loss: 0.6125 step time: 0.2215\n",
      "epoch 4 average loss: 0.5987\n",
      "saved new best metric model\n",
      "current epoch: 4 current mean dice: 0.0707 best mean dice: 0.0707 at epoch: 4\n",
      "time consuming of epoch 4 is: 8.9269\n",
      "----------\n",
      "epoch 5/20\n",
      "1/32, train_loss: 0.5993 step time: 0.1424\n",
      "2/32, train_loss: 0.5740 step time: 0.1474\n",
      "3/32, train_loss: 0.5173 step time: 0.1766\n",
      "4/32, train_loss: 0.5775 step time: 0.1456\n",
      "5/32, train_loss: 0.5875 step time: 0.1673\n",
      "6/32, train_loss: 0.6010 step time: 0.1601\n",
      "7/32, train_loss: 0.6087 step time: 0.1489\n",
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      "9/32, train_loss: 0.6024 step time: 0.1484\n",
      "10/32, train_loss: 0.6208 step time: 0.1475\n",
      "11/32, train_loss: 0.5951 step time: 0.1473\n",
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      "13/32, train_loss: 0.5850 step time: 0.1453\n",
      "14/32, train_loss: 0.5943 step time: 0.1466\n",
      "15/32, train_loss: 0.5823 step time: 0.2708\n",
      "16/32, train_loss: 0.6360 step time: 0.1414\n",
      "17/32, train_loss: 0.6025 step time: 0.1628\n",
      "18/32, train_loss: 0.5814 step time: 0.1519\n",
      "19/32, train_loss: 0.5453 step time: 0.1471\n",
      "20/32, train_loss: 0.5917 step time: 0.1483\n",
      "21/32, train_loss: 0.5615 step time: 0.1513\n",
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      "23/32, train_loss: 0.5463 step time: 0.1490\n",
      "24/32, train_loss: 0.5792 step time: 0.1408\n",
      "25/32, train_loss: 0.5839 step time: 0.1822\n",
      "26/32, train_loss: 0.5778 step time: 0.1471\n",
      "27/32, train_loss: 0.6299 step time: 0.1505\n",
      "28/32, train_loss: 0.5749 step time: 0.1512\n",
      "29/32, train_loss: 0.5769 step time: 0.1490\n",
      "30/32, train_loss: 0.5880 step time: 0.1492\n",
      "31/32, train_loss: 0.5583 step time: 0.1455\n",
      "32/32, train_loss: 0.5488 step time: 0.1445\n",
      "epoch 5 average loss: 0.5843\n",
      "current epoch: 5 current mean dice: 0.0662 best mean dice: 0.0707 at epoch: 4\n",
      "time consuming of epoch 5 is: 8.9328\n",
      "----------\n",
      "epoch 6/20\n",
      "1/32, train_loss: 0.5250 step time: 0.1391\n",
      "2/32, train_loss: 0.5471 step time: 0.1793\n",
      "3/32, train_loss: 0.5642 step time: 0.1717\n",
      "4/32, train_loss: 0.5248 step time: 0.1595\n",
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      "6/32, train_loss: 0.5830 step time: 0.1478\n",
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      "10/32, train_loss: 0.5495 step time: 0.1576\n",
      "11/32, train_loss: 0.5433 step time: 0.1524\n",
      "12/32, train_loss: 0.6055 step time: 0.1525\n",
      "13/32, train_loss: 0.5648 step time: 0.1441\n",
      "14/32, train_loss: 0.5691 step time: 0.1793\n",
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      "16/32, train_loss: 0.5527 step time: 0.1519\n",
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      "22/32, train_loss: 0.5991 step time: 0.3011\n",
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      "24/32, train_loss: 0.5589 step time: 0.1420\n",
      "25/32, train_loss: 0.6071 step time: 0.1950\n",
      "26/32, train_loss: 0.5665 step time: 0.1646\n",
      "27/32, train_loss: 0.5841 step time: 0.1458\n",
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      "29/32, train_loss: 0.5885 step time: 0.1518\n",
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      "31/32, train_loss: 0.5797 step time: 0.1519\n",
      "32/32, train_loss: 0.5589 step time: 0.1497\n",
      "epoch 6 average loss: 0.5708\n",
      "saved new best metric model\n",
      "current epoch: 6 current mean dice: 0.0770 best mean dice: 0.0770 at epoch: 6\n",
      "time consuming of epoch 6 is: 9.1793\n",
      "----------\n",
      "epoch 7/20\n",
      "1/32, train_loss: 0.5887 step time: 0.1410\n",
      "2/32, train_loss: 0.5602 step time: 0.1686\n",
      "3/32, train_loss: 0.5926 step time: 0.1482\n",
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      "32/32, train_loss: 0.5281 step time: 0.1458\n",
      "epoch 7 average loss: 0.5579\n",
      "saved new best metric model\n",
      "current epoch: 7 current mean dice: 0.0900 best mean dice: 0.0900 at epoch: 7\n",
      "time consuming of epoch 7 is: 8.7140\n",
      "----------\n",
      "epoch 8/20\n",
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      "epoch 8 average loss: 0.5437\n",
      "current epoch: 8 current mean dice: 0.0793 best mean dice: 0.0900 at epoch: 7\n",
      "time consuming of epoch 8 is: 9.0853\n",
      "----------\n",
      "epoch 9/20\n",
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      "epoch 9 average loss: 0.5319\n",
      "current epoch: 9 current mean dice: 0.0762 best mean dice: 0.0900 at epoch: 7\n",
      "time consuming of epoch 9 is: 8.8652\n",
      "----------\n",
      "epoch 10/20\n",
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      "epoch 10 average loss: 0.5286\n",
      "current epoch: 10 current mean dice: 0.0858 best mean dice: 0.0900 at epoch: 7\n",
      "time consuming of epoch 10 is: 8.8002\n",
      "----------\n",
      "epoch 11/20\n",
      "1/32, train_loss: 0.5256 step time: 0.1413\n",
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      "32/32, train_loss: 0.5076 step time: 0.1447\n",
      "epoch 11 average loss: 0.5202\n",
      "saved new best metric model\n",
      "current epoch: 11 current mean dice: 0.0979 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 11 is: 9.0287\n",
      "----------\n",
      "epoch 12/20\n",
      "1/32, train_loss: 0.5290 step time: 0.1401\n",
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      "epoch 12 average loss: 0.5072\n",
      "current epoch: 12 current mean dice: 0.0571 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 12 is: 8.7471\n",
      "----------\n",
      "epoch 13/20\n",
      "1/32, train_loss: 0.4936 step time: 0.1388\n",
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      "epoch 13 average loss: 0.4923\n",
      "current epoch: 13 current mean dice: 0.0625 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 13 is: 8.8664\n",
      "----------\n",
      "epoch 14/20\n",
      "1/32, train_loss: 0.5397 step time: 0.1413\n",
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      "epoch 14 average loss: 0.4867\n",
      "current epoch: 14 current mean dice: 0.0718 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 14 is: 9.2073\n",
      "----------\n",
      "epoch 15/20\n",
      "1/32, train_loss: 0.4804 step time: 0.1402\n",
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      "epoch 15 average loss: 0.4753\n",
      "current epoch: 15 current mean dice: 0.0720 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 15 is: 9.0329\n",
      "----------\n",
      "epoch 16/20\n",
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      "21/32, train_loss: 0.5103 step time: 0.1577\n",
      "22/32, train_loss: 0.4981 step time: 0.1440\n",
      "23/32, train_loss: 0.4929 step time: 0.1803\n",
      "24/32, train_loss: 0.4490 step time: 0.1699\n",
      "25/32, train_loss: 0.4760 step time: 0.1497\n",
      "26/32, train_loss: 0.4297 step time: 0.1848\n",
      "27/32, train_loss: 0.4073 step time: 0.1762\n",
      "28/32, train_loss: 0.4568 step time: 0.1505\n",
      "29/32, train_loss: 0.4152 step time: 0.1519\n",
      "30/32, train_loss: 0.4303 step time: 0.1507\n",
      "31/32, train_loss: 0.3782 step time: 0.1508\n",
      "32/32, train_loss: 0.4012 step time: 0.1480\n",
      "epoch 16 average loss: 0.4629\n",
      "current epoch: 16 current mean dice: 0.0592 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 16 is: 9.3627\n",
      "----------\n",
      "epoch 17/20\n",
      "1/32, train_loss: 0.4849 step time: 0.1390\n",
      "2/32, train_loss: 0.4662 step time: 0.1779\n",
      "3/32, train_loss: 0.4382 step time: 0.1564\n",
      "4/32, train_loss: 0.4719 step time: 0.1536\n",
      "5/32, train_loss: 0.5191 step time: 0.1451\n",
      "6/32, train_loss: 0.5246 step time: 0.1833\n",
      "7/32, train_loss: 0.4467 step time: 0.1493\n",
      "8/32, train_loss: 0.4489 step time: 0.1469\n",
      "9/32, train_loss: 0.4388 step time: 0.1474\n",
      "10/32, train_loss: 0.5182 step time: 0.1487\n",
      "11/32, train_loss: 0.4202 step time: 0.1480\n",
      "12/32, train_loss: 0.4665 step time: 0.1460\n",
      "13/32, train_loss: 0.3792 step time: 0.1462\n",
      "14/32, train_loss: 0.4259 step time: 0.1507\n",
      "15/32, train_loss: 0.3706 step time: 0.1503\n",
      "16/32, train_loss: 0.5094 step time: 0.1492\n",
      "17/32, train_loss: 0.4735 step time: 0.1514\n",
      "18/32, train_loss: 0.3956 step time: 0.1475\n",
      "19/32, train_loss: 0.4781 step time: 0.1486\n",
      "20/32, train_loss: 0.4049 step time: 0.1491\n",
      "21/32, train_loss: 0.5104 step time: 0.1481\n",
      "22/32, train_loss: 0.5646 step time: 0.1490\n",
      "23/32, train_loss: 0.4800 step time: 0.1471\n",
      "24/32, train_loss: 0.4602 step time: 0.1488\n",
      "25/32, train_loss: 0.4357 step time: 0.1459\n",
      "26/32, train_loss: 0.5592 step time: 0.1997\n",
      "27/32, train_loss: 0.3842 step time: 0.1496\n",
      "28/32, train_loss: 0.4294 step time: 0.1798\n",
      "29/32, train_loss: 0.4408 step time: 0.1959\n",
      "30/32, train_loss: 0.4187 step time: 0.2407\n",
      "31/32, train_loss: 0.4140 step time: 0.1427\n",
      "32/32, train_loss: 0.4280 step time: 0.1704\n",
      "epoch 17 average loss: 0.4565\n",
      "current epoch: 17 current mean dice: 0.0844 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 17 is: 9.0729\n",
      "----------\n",
      "epoch 18/20\n",
      "1/32, train_loss: 0.4320 step time: 0.1381\n",
      "2/32, train_loss: 0.4703 step time: 0.1835\n",
      "3/32, train_loss: 0.3749 step time: 0.1593\n",
      "4/32, train_loss: 0.4458 step time: 0.1482\n",
      "5/32, train_loss: 0.4361 step time: 0.1492\n",
      "6/32, train_loss: 0.4424 step time: 0.1520\n",
      "7/32, train_loss: 0.4379 step time: 0.1526\n",
      "8/32, train_loss: 0.4877 step time: 0.1503\n",
      "9/32, train_loss: 0.4465 step time: 0.1490\n",
      "10/32, train_loss: 0.4148 step time: 0.1483\n",
      "11/32, train_loss: 0.3468 step time: 0.1493\n",
      "12/32, train_loss: 0.4200 step time: 0.1477\n",
      "13/32, train_loss: 0.3988 step time: 0.1476\n",
      "14/32, train_loss: 0.4606 step time: 0.1492\n",
      "15/32, train_loss: 0.4258 step time: 0.1484\n",
      "16/32, train_loss: 0.5216 step time: 0.1497\n",
      "17/32, train_loss: 0.4428 step time: 0.1470\n",
      "18/32, train_loss: 0.4133 step time: 0.2569\n",
      "19/32, train_loss: 0.4531 step time: 0.1445\n",
      "20/32, train_loss: 0.4098 step time: 0.1707\n",
      "21/32, train_loss: 0.4593 step time: 0.1636\n",
      "22/32, train_loss: 0.4427 step time: 0.1546\n",
      "23/32, train_loss: 0.4765 step time: 0.1472\n",
      "24/32, train_loss: 0.4004 step time: 0.1694\n",
      "25/32, train_loss: 0.3195 step time: 0.1565\n",
      "26/32, train_loss: 0.4769 step time: 0.1509\n",
      "27/32, train_loss: 0.3303 step time: 0.1493\n",
      "28/32, train_loss: 0.3761 step time: 0.1469\n",
      "29/32, train_loss: 0.4529 step time: 0.1492\n",
      "30/32, train_loss: 0.3828 step time: 0.1531\n",
      "31/32, train_loss: 0.4671 step time: 0.1465\n",
      "32/32, train_loss: 0.3750 step time: 0.1514\n",
      "epoch 18 average loss: 0.4263\n",
      "current epoch: 18 current mean dice: 0.0565 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 18 is: 9.1809\n",
      "----------\n",
      "epoch 19/20\n",
      "1/32, train_loss: 0.4674 step time: 0.1389\n",
      "2/32, train_loss: 0.4574 step time: 0.1547\n",
      "3/32, train_loss: 0.4389 step time: 0.1682\n",
      "4/32, train_loss: 0.3548 step time: 0.1568\n",
      "5/32, train_loss: 0.4827 step time: 0.1582\n",
      "6/32, train_loss: 0.4136 step time: 0.1550\n",
      "7/32, train_loss: 0.4607 step time: 0.1517\n",
      "8/32, train_loss: 0.4111 step time: 0.1527\n",
      "9/32, train_loss: 0.4343 step time: 0.1485\n",
      "10/32, train_loss: 0.3557 step time: 0.1511\n",
      "11/32, train_loss: 0.3671 step time: 0.1481\n",
      "12/32, train_loss: 0.4594 step time: 0.1497\n",
      "13/32, train_loss: 0.4028 step time: 0.1499\n",
      "14/32, train_loss: 0.4989 step time: 0.1485\n",
      "15/32, train_loss: 0.4203 step time: 0.1474\n",
      "16/32, train_loss: 0.3896 step time: 0.1459\n",
      "17/32, train_loss: 0.4065 step time: 0.1461\n",
      "18/32, train_loss: 0.4036 step time: 0.1493\n",
      "19/32, train_loss: 0.5184 step time: 0.1488\n",
      "20/32, train_loss: 0.3987 step time: 0.1461\n",
      "21/32, train_loss: 0.4638 step time: 0.1474\n",
      "22/32, train_loss: 0.5178 step time: 0.1523\n",
      "23/32, train_loss: 0.4702 step time: 0.1650\n",
      "24/32, train_loss: 0.4475 step time: 0.1582\n",
      "25/32, train_loss: 0.3167 step time: 0.1576\n",
      "26/32, train_loss: 0.4777 step time: 0.1498\n",
      "27/32, train_loss: 0.3607 step time: 0.1934\n",
      "28/32, train_loss: 0.3887 step time: 0.1714\n",
      "29/32, train_loss: 0.4242 step time: 0.1476\n",
      "30/32, train_loss: 0.3924 step time: 0.1525\n",
      "31/32, train_loss: 0.3442 step time: 0.1477\n",
      "32/32, train_loss: 0.4097 step time: 0.1575\n",
      "epoch 19 average loss: 0.4236\n",
      "current epoch: 19 current mean dice: 0.0587 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 19 is: 8.8046\n",
      "----------\n",
      "epoch 20/20\n",
      "1/32, train_loss: 0.4267 step time: 0.1402\n",
      "2/32, train_loss: 0.3331 step time: 0.1566\n",
      "3/32, train_loss: 0.4313 step time: 0.1611\n",
      "4/32, train_loss: 0.4721 step time: 0.1462\n",
      "5/32, train_loss: 0.4230 step time: 0.1501\n",
      "6/32, train_loss: 0.4502 step time: 0.1492\n",
      "7/32, train_loss: 0.4664 step time: 0.1511\n",
      "8/32, train_loss: 0.4361 step time: 0.1477\n",
      "9/32, train_loss: 0.4531 step time: 0.1494\n",
      "10/32, train_loss: 0.5041 step time: 0.1521\n",
      "11/32, train_loss: 0.4069 step time: 0.1514\n",
      "12/32, train_loss: 0.4289 step time: 0.1492\n",
      "13/32, train_loss: 0.3243 step time: 0.1522\n",
      "14/32, train_loss: 0.4391 step time: 0.1482\n",
      "15/32, train_loss: 0.3522 step time: 0.1480\n",
      "16/32, train_loss: 0.3283 step time: 0.1589\n",
      "17/32, train_loss: 0.4371 step time: 0.1712\n",
      "18/32, train_loss: 0.4571 step time: 0.1684\n",
      "19/32, train_loss: 0.3773 step time: 0.1457\n",
      "20/32, train_loss: 0.3144 step time: 0.1923\n",
      "21/32, train_loss: 0.4827 step time: 0.1518\n",
      "22/32, train_loss: 0.3970 step time: 0.1536\n",
      "23/32, train_loss: 0.4156 step time: 0.1501\n",
      "24/32, train_loss: 0.4109 step time: 0.1525\n",
      "25/32, train_loss: 0.3335 step time: 0.1484\n",
      "26/32, train_loss: 0.4498 step time: 0.1472\n",
      "27/32, train_loss: 0.4648 step time: 0.1474\n",
      "28/32, train_loss: 0.4017 step time: 0.1470\n",
      "29/32, train_loss: 0.3039 step time: 0.1482\n",
      "30/32, train_loss: 0.4028 step time: 0.1483\n",
      "31/32, train_loss: 0.4107 step time: 0.1530\n",
      "32/32, train_loss: 0.3292 step time: 0.1504\n",
      "epoch 20 average loss: 0.4083\n",
      "current epoch: 20 current mean dice: 0.0432 best mean dice: 0.0979 at epoch: 11\n",
      "time consuming of epoch 20 is: 9.0662\n",
      "train completed, best_metric: 0.0979 at epoch: 11 total time: 179.1661\n",
      "total training time of 20 epochs without AMP: 204.5231\n"
     ]
    }
   ],
   "source": [
    "set_determinism(seed=0)\n",
    "start = time.time()\n",
    "(\n",
    "    max_epochs,\n",
    "    epoch_loss_values,\n",
    "    metric_values,\n",
    "    epoch_times,\n",
    "    best,\n",
    ") = train_process(amp=False)\n",
    "total_time = time.time() - start\n",
    "print(f\"total training time of {max_epochs} epochs without AMP: {total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training without AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NVIDIA GeForce RTX 3080 Ti\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 1         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      | 320420 KiB |   1827 MiB |  11928 GiB |  11927 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         | 320420 KiB |   1827 MiB |  11928 GiB |  11927 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Requested memory      | 318667 KiB |   1821 MiB |  11903 GiB |  11903 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |   2822 MiB |   3108 MiB |   3516 MiB | 710656 KiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory | 461916 KiB |   1254 MiB |  10639 GiB |  10638 GiB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     449    |     995    |    1196 K  |    1196 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     449    |     995    |    1196 K  |    1196 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      39    |      51    |      55    |      16    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      32    |      75    |  526947    |  526915    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize allocations  |       0    |       0    |       0    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Oversize GPU segments |       0    |       0    |       0    |       0    |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title(\"Regular Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title(\"Regular Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title(\"AMP Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(amp_epoch_loss_values))]\n",
    "y = amp_epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title(\"AMP Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(amp_metric_values))]\n",
    "y = amp_metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Train Time(300 epochs)\")\n",
    "plt.bar(\"regular\", total_time, 1, label=\"Regular training\", color=\"red\")\n",
    "plt.bar(\"AMP\", amp_total_time, 1, label=\"AMP training\", color=\"green\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Epoch Time\")\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.plot(x, epoch_times, label=\"Regular training\", color=\"red\")\n",
    "plt.plot(x, amp_epoch_times, label=\"AMP training\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time to achieve metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_best_metric_time(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[2][i]\n",
    "    return -1\n",
    "\n",
    "\n",
    "def get_best_metric_epochs(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[1][i]\n",
    "    return -1\n",
    "\n",
    "\n",
    "plt.set_loglevel(\"WARNING\")\n",
    "\n",
    "plt.figure(\"train\", (18, 6))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Metrics Time\")\n",
    "plt.xlabel(\"secs\")\n",
    "plt.ylabel(\"best mean_dice\")\n",
    "plt.plot(best[2], best[0], label=\"Regular training\", color=\"red\")\n",
    "plt.plot(amp_best[2], amp_best[0], label=\"AMP training\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Typical Metrics Time\")\n",
    "plt.xlabel(\"best mean_dice\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.bar(\n",
    "    \"0.80\",\n",
    "    get_best_metric_time(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_time(0.8, amp_best),\n",
    "    0.5,\n",
    "    label=\"AMP training\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.bar(\"0.85\", get_best_metric_time(0.85, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.85 \", get_best_metric_time(0.85, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.90\", get_best_metric_time(0.9, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.90 \", get_best_metric_time(0.9, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_time(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_time(0.93, amp_best), 0.5, color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Typical Metrics Epochs\")\n",
    "plt.xlabel(\"best mean_dice\")\n",
    "plt.ylabel(\"epochs\")\n",
    "plt.bar(\n",
    "    \"0.80\",\n",
    "    get_best_metric_epochs(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_epochs(0.8, amp_best),\n",
    "    0.5,\n",
    "    label=\"AMP training\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.bar(\"0.85\", get_best_metric_epochs(0.85, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.85 \", get_best_metric_epochs(0.85, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.90\", get_best_metric_epochs(0.9, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.90 \", get_best_metric_epochs(0.9, amp_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_epochs(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_epochs(0.93, amp_best), 0.5, color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
